OrgMining 2.0: A Novel Framework for Organizational Model Mining from
Event Logs
- URL: http://arxiv.org/abs/2011.12445v2
- Date: Thu, 26 Nov 2020 02:15:39 GMT
- Title: OrgMining 2.0: A Novel Framework for Organizational Model Mining from
Event Logs
- Authors: Jing Yang, Chun Ouyang, Wil M.P. van der Aalst, Arthur H.M. ter
Hofstede, Yang Yu
- Abstract summary: We develop a novel framework built upon a richer definition of organizational models coupling resource grouping with process execution knowledge.
We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery.
- Score: 6.558355743287404
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Providing appropriate structures around human resources can streamline
operations and thus facilitate the competitiveness of an organization. To
achieve this goal, modern organizations need to acquire an accurate and timely
understanding of human resource grouping while faced with an ever-changing
environment. The use of process mining offers a promising way to help address
the need through utilizing event log data stored in information systems. By
extracting knowledge about the actual behavior of resources participating in
business processes from event logs, organizational models can be constructed,
which facilitate the analysis of the de facto grouping of human resources
relevant to process execution. Nevertheless, open research gaps remain to be
addressed when applying the state-of-the-art process mining to analyze resource
grouping. For one, the discovery of organizational models has only limited
connections with the context of process execution. For another, a rigorous
solution that evaluates organizational models against event log data is yet to
be proposed. In this paper, we aim to tackle these research challenges by
developing a novel framework built upon a richer definition of organizational
models coupling resource grouping with process execution knowledge. By
introducing notions of conformance checking for organizational models, the
framework allows effective evaluation of organizational models, and therefore
provides a foundation for analyzing and improving resource grouping based on
event logs. We demonstrate the feasibility of this framework by proposing an
approach underpinned by the framework for organizational model discovery, and
also conduct experiments on real-life event logs to discover and evaluate
organizational models.
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